Field of the Invention
The present invention relates to methods and systems for using interactive evolutionary computing for conducting product optimization and consumer research over computer-based networks, more specifically, to methods and systems for providing a participant in such research with a more engaging experience through increased control over the evolutionary algorithm.
Description of the Related Art
A common challenge faced by marketers and developers of products and services is identifying the optimal combination of features or attributes that will maximize the likelihood of market success. This challenge is typically compounded by the availability of a large number of variants or levels for each of the product attributes under consideration. This often results in a space of possible product forms or configurations reaching into the millions.
In a typical product category, the marketer/developer may be considering between 5 and 10 or more attributes, each of which could take on between 2 and 25 different levels or variants, possibly more. For example, in a consumer product such as a laundry detergent, the attributes of interest might include the product brand and sub-brand, the positioning of the product (the need the product is meant to address), product benefits claims, such as its cleaning power, or its ability to protect different fabrics. Other product features may include its scent, the package form, colors and graphics, in addition to package size and price.
Over the last several decades, conjoint analysis, in its various forms, has become the standard quantitative methodology for conducting market research in such situations, in order to understand consumers' preferences and trade-offs among the various attribute variants under consideration. Preference or choice data obtained from conjoint studies are used to develop statistical preference or choice models, which are then used to make inferences regarding the preferred form of the proposed product or service for a given group of consumers.
In earlier forms of conjoint studies, respondents were presented with a number of product alternatives (often referred to as product profiles). The respondents were then asked to rank or rate them (on a numerical scale from 1 to 10, e.g.), based on their degree of liking or purchase interest. The various profiles would either be given to the respondents all at once, in the case of a sorting exercise, or one at time, as in certain computer implementations of ratings-based conjoint.
Over the last decade however, choice-based conjoint has become the preferred methodology. In choice-based conjoint, the respondent is presented with a choice set containing a small number of product alternatives (typically 2 to 4), and asked to choose the preferred one in that set; that is, the alternative that is most appealing to him, or the one that he would be most likely to purchase. The process is then repeated a number of times, such that the respondent ends up processing between 6 and 20 choice sets typically.
From the point of view of the respondent task, choice-based conjoint offers a few advantages over earlier approaches. First, the choice task more closely mimics the decision process that a consumer faces when choosing a product at the point of purchase. Secondly, it is an easier task cognitively, as the respondent only needs to consider a few alternatives at time, and he does not need to formulate in his mind a rating scale that accurately covers all the alternatives presented (and yet to be presented) during the study.
In general, regardless of the conjoint method used, the product profiles presented to the respondents include specific variants or levels for all the attributes under consideration (full-profile studies). In certain specialized cases however, some attributes are left out of part of the study, in order to manage the cognitive load imposed on the respondent (partial-profile studies). In either case however, the respondent is supposed to formulate a preference or a choice decision based on the product profile as a whole. The respondent is supposed to take into account all the attributes in the product profile, and make the relevant tradeoffs between them (a definitional aspect of “conjoint”, which derives from the two words “consider” and “jointly”).
Despite the advantages of the choice task described above, respondents still complain that these studies are tedious, repetitive, even frustrating. Part of that frustration and tedium is due to the fact that a respondent is typically presented with a pre-determined set of choices based on an experimental design (that is, based on a statistical Design of Experiments or DoE), which is not responsive to his stated preferences or choices, but rather optimized to minimize bias and maximize coverage of the design space. Beyond that, a respondent often faces the frustrating situation where his preferred alternative in a choice set is somewhat flawed due to a particular attribute variant he dislikes, yet next to it is an overall less preferred alternative that contains a better variant for the attribute in question. Another frustrating situation is one where, in a previous choice set, the respondent came across (and even selected) an alternative that did contain a more preferred variant for that same attribute.
Advances in computer graphical user interfaces have made it possible to improve the design and presentation of choice sets, as well as the respondent interaction during the choice process. In particular, as the Internet has become the more commonly used medium for this type of research, browser-hosted conjoint surveys with dynamic web pages have become the norm. However, these improvements have been mostly aesthetic and superficial, and have not changed the fundamental nature of the respondent interaction, which remains a choice task from among a few product representations.
It should be noted that some conjoint-based market research methodologies do involve obtaining attribute-level preference information from the respondent. One example is the Adaptive Choice-Based Conjoint (Adaptive CBC) systems commercialized by Sawtooth Software of Sequim, Wash. (http://www.sawtoothsoftware.com). That methodology relies on a questionnaire about attribute importance and acceptable or preferred attribute variants. This questionnaire precedes the conjoint part of the interview, and it is used to limit the range of choices presented to the respondent in the conjoint exercise proper, during which attribute-level feedback or selection are not possible. These approaches do not eliminate the problems outlined above, and some have been found to have methodological flaws.
To a great extent, the respondent task in a conjoint study is constrained by the nature of the statistical model that the researcher is able to estimate following the data collection phase. The most commonly used model in choice-based conjoint is the logistic regression based probabilistic choice model. This model only requires that the respondent make a single selection in every choice set. In the case where additional choices are made per choice set, i.e., if the respondent has provided a ranking (full or partial) of the alternatives in the set, a more powerful rank-ordered logistic (or Plackett-Luce) model can be estimated. Models that are able to represent more complex decision processes, such as the use of lexicographic rules or nonlinear processes involving utility thresholds are rarely used in practice and mostly remain the purview of academic research.
These constraints are becoming increasingly limiting in practice, as market researchers seek to answer the challenge of keeping online respondents interested in, and focused on, their tasks, and ensuring that the data collected is accurate. And they are becoming especially problematic as the lines between traditional, psychometrics inspired quantitative market research and modern, “Web 2.0” inspired crowd-sourcing and co-creation are blurring.
Several years ago, certain product companies started providing online product configurators to allow their customers to customize a product such as a personal computer, prior to placing an order. An early example was the website of the Dell personal computer company of Round Rock, Tex. As such sites proliferated, researchers started working on methods and techniques for deriving consumer preference models from configurator data, as described in U.S. Pat. No. 7,398,233 granted to Bayer et al. The hope was to also use such configurators to conduct prospective market research, involving products and product attributes not yet in the market. However, attempts at developing quantitative market research methodologies based on this early form of individual-level co-creation interaction have not met with much success, and haven't had much of an impact in practice. The reason has been the difficulty in bridging the gap between the nature of the data obtained from configurators and the type of data required for choice modeling.
An alternative methodology for finding consumer-preferred product alternatives within a large combinatorial space was developed using interactive evolutionary computation. That approach does not rely on the researcher postulating a choice or preference model that represents the consumer's decision process, nor does it make any a priori assumptions about the structure of any such models. Rather, it relies on direct respondent feedback to drive a heuristic search and optimization process in real-time, during the data collection process itself. The approach is described in U.S. Pat. Nos. 7,016,882, 7,177,851, 7,308,418, 7,610,249, and 7,730,002 which are incorporated herein by reference.
The use of interactive evolutionary computation to identify optimal or near-optimal, strongly preferred product concepts offers several advantages. These derive from not having to estimate a statistical model, and thus not being constrained to collect data in a format optimized for that purpose. Some of these advantages are summarized below.                A first advantage is the ability to search larger spaces of possibilities (spaces with more attributes and/or more variants per attribute), thanks to the built-in parallelism and the adaptive nature of evolutionary search and optimization. Evolutionary algorithms deploy a population of solution candidates at once, which effectively parallelizes the process and increases its efficiency. Evolutionary algorithms, by the nature of their operations, tend to increasingly focus the search around those areas of the space that are more promising in terms of fitness or performance. By contrast, approaches that are purely model-based follow a DoE whereby as much data is collected for promising attribute variants as for variants that are disliked by consumers.        A second advantage is that the optimal or near-optimal concepts identified using evolutionary computation reflect synergies between the variants of different attributes. During the optimization process, the evolutionary algorithm and its operators will assemble complete genomes and assess their fitness. Attribute variants that do not work well together will receive low fitness scores, and these genomes or parts thereof will be removed from the population of solutions as the search proceeds. By contrast, most conjoint models used in practice are main-effects-only models, which do not include interaction terms to model these synergies. This is done for practical considerations as, in a typical study, adding even two-way interactions to the choice model could easily increase the number of parameters by an order of magnitude. This makes it impossible to estimate such a model given the amount of data that can reasonably be collected from each respondent, and the number of respondents available per study. This advantage is particularly important in the case of aesthetic attributes, where the harmony between different product attributes can be as important as the preference for the attributes on their own, as is the case with color combinations, for example.        A third advantage is the ability to impose complex rules and constraints among attribute variants. These typically reflect engineering, manufacturing, or cost limitations, whereby certain combinations of attributes cannot be produced, or at least not economically. They could be driven by legal limitations as in the case of benefit statements or efficacy claims for food or drugs. They could also be based on branding or product positioning goals. Although it is possible to impose a handful of prohibitions in a conjoint study, to avoid skewing respondents' choices by presenting them with impossible combinations, these constraints have a deleterious effect on the design of the study (the DoE used to field the study), and subsequently on the properties of the resulting choice model, such as biased estimates and collinearity problems.        A related advantage is the ability to define complex types of attributes, such as combination attributes and permutation attributes, where several variants are combined and presented together in a given concept profile (either as an unordered or an ordered list).        Finally, the preferred concepts identified through interactive evolutionary computation are concepts that would have been seen several times by a number of participants, if the algorithm is so tuned. This is usually not the case in model based research techniques, where presumptive preferred concepts are synthesized after-the-fact, based on inferences from a statistical model. Most likely, these concepts were not part of the test concepts generated by the DoE, and were therefore never seen by any respondent.        
Most importantly for the present embodiments, the absence of an explicit choice model relaxes some the constraints discussed earlier regarding the respondent's task. So it becomes possible to get feedback from the respondent on specific attributes and variants and to make use of it during the optimization process. And it becomes possible to deploy interaction techniques for overcoming the tedium of a pure choice task.
Instead of presenting the respondent with a set of product forms on the computer screen, and asking him to select the preferred form or forms on that screen, the user interactions modes disclosed in the present embodiments allow the participant to make changes to the makeup of the attributes of the preferred product form(s) before, during, or after the choice task. This takes the participant from the role of selector to a role of “co-creator”, by allowing him to provide preference information at the product attribute level, and thus giving him more direct control on the underlying search and optimization process.